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Evaluating the presence of a slight amount of water plays a crucial role in practical applications such as the advanced detection of dew condensation and the microdetermination of perspiration and transpiration. For this purpose, we have developed a configuration for the moisture sensor that consists of a microgalvanic cell composed of narrow metal arrays. It is inferred that the output response current arising from this sensor should depend on the geometric parameters (e.g., number, area, volume, etc.) of water droplets attaching on the sensor surface. In this study, the output current was recorded, while the microscopic images of the sensor surface were captured. The droplets on the sensor surface were analyzed manually and by computational image processing with deep learning and ImageJ. The deep learning technique shortened the processing time to 1/1000 of the manual one and was able to match 90-100% of the manual count. The results revealed that the response current increased with the total projected area of droplets bridging the galvanic-coupled arrays on the sensor surface. In addition, a straight line with relatively strong positive correlation was obtained between the response current and the total volume of the bridging droplets. These findings suggested that our sensor can be practically used to estimate the presence of a slight amount of water.

Quantitative Correlation of Droplets on Galvanic-Coupled Arrays with Response Current by Image Processing